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Pittsburgh Response to Endovascular therapy (PRE) score: optimizing patient selection for endovascular therapy for large vessel occlusion strokes

2014· article· en· W2130503620 on OpenAlex
Srikant Rangaraju, Amin Aghaebrahim, Christopher Streib, Chung-Huan J. Sun, Marc Ribó, Marián Muchada, Raul G. Nogueira, Michael Frankel, Rishi Gupta, Ashutosh P. Jadhav, Tudor G. Jovin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of NeuroInterventional Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersStryker
KeywordsMedicineModified Rankin ScaleCohortLogistic regressionStroke (engine)QuartileInternal medicineOcclusionPhysical therapyIschemic strokeConfidence intervalIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: Endovascular therapy seems to benefit a subset of patients with large vessel occlusion strokes. We aimed to develop a clinically useful tool to identify patients who are likely to benefit from endovascular therapy. METHODS: In a derivation cohort of consecutively treated patients with anterior circulation large vessel occlusion (Grady Memorial Hospital, N=247), independent predictors (p<0.1) of good outcome (90-day modified Rankin scale score (mRS) 0-2) were determined using logistic regression to derive the Pittsburgh Response to Endovascular therapy (PRE) score as a predictor of good outcome. The PRE score was validated in two institutional cohorts (University of Pittsburgh Medical Center (UPMC): N=393; Unitat d'Ictus Vall d'Hebron: N=204) and its discriminative power for good outcome was compared with other validated tools. Benefit of successful recanalization was assessed in PRE score groups. RESULTS: Independent predictors of good outcome in the derivation cohort (age, baseline National Institute of Health Stroke Scale (NIHSS) score and Alberta Stroke Program Early CT Score (ASPECTS)) were used in the model: PRE score=age (years)+2×NIHSS-10 × ASPECTS. PRE score was highly predictive of good outcome in the derivation cohort (area under the curve (AUC)=0.79) and validation cohorts (UPMC: AUC=0.79; UIVH: AUC=0.72) with comparable rates of good outcome in all PRE risk quartiles. PRE was superior to Totaled Health Risks In Vascular Events (THRIVE) (p=0.03) and Stroke Prognostication using Age and NIHSS (SPAN) (p=0.007), with a trend towards superiority to Houston Intra-Arterial Therapy 2 (HIAT2) (p=0.06) and iSCORE (p=0.051) in predicting good outcomes. Better outcomes were associated with successful recanalization in patients with PRE scores -24 to +49 but not in patients with PRE scores <-24 or ≥ 50. CONCLUSIONS: The PRE score is a validated tool that predicts outcomes and may facilitate patient selection for endovascular therapy in anterior circulation large vessel occlusions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.284
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it